图像标题基于语义场景的图像标题
Fengzhi Zhao1,2, Zhezhou Yu1,2,3, Tao Wang1,2
1College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Entropy (Basel, Switzerland)
|October 25, 2024
概括
语义场景编码器 (SSE) 通过整合场景和语义图表来改进图像标题,为复杂的视觉数据生成更准确和更全面的描述.
科学领域:
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 图像标题生成图像的文字描述,对于图像检索和自动驾驶等应用至关重要.
- 现有的基于地区的方法往往侧重于地方特征,忽视了整体场景理解,导致复杂场景的字幕不准确.
- 当前的方法难以提取完整的语义信息,导致有偏见或缺陷的标题.
研究的目的:
- 为了解决现有的图像标题方法的局限性.
- 提出一种新的语义场景编码器 (SSE),用于生成全面而准确的图像字幕.
- 增强对图像内容和语义关系的理解,以改善字幕生成.
主要方法:
- 语义场景编码器 (SSE) 从图像中提取场景图,并将其集成到图像信息编码中.
- 从字幕中提取一个语义图,通过可学习的注意力机制称为"词典"来保存信息.
- 该模型结合了编码的图像信息和学习的语义信息,用于标题生成.
主要成果:
- 在MSCOCO数据集上评估了SSE模型.
- 实验结果表明,生成的标题的整体质量有了显著的改善.
- 在多个评估指标中,SSE获得了更高的分数,这表明在图像标题中表现出色.
结论:
- 拟议的语义场景编码器 (SSE) 通过结合场景和语义图形信息,有效地增强了图像标题.
- 通过考虑全球场景背景和完整的语义信息,SSE克服了以前方法的局限性.
- 该模型在生成准确和连贯的标题方面具有显著的优势,特别是在复杂的视觉场景中.
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